March 13, 2024, 4:42 a.m. | Marek Elias, Haim Kaplan, Yishay Mansour, Shay Moran

cs.LG updates on

arXiv:2403.07413v1 Announce Type: new
Abstract: Recent advances in algorithmic design show how to utilize predictions obtained by machine learning models from past and present data. These approaches have demonstrated an enhancement in performance when the predictions are accurate, while also ensuring robustness by providing worst-case guarantees when predictions fail. In this paper we focus on online problems; prior research in this context was focused on a paradigm where the predictor is pre-trained on past data and then used as a …

abstract advances algorithms arxiv case cs.ds cs.lg data design focus machine machine learning machine learning models paper performance predictions robustness show type

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